Overview

Dataset statistics

Number of variables10
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory89.5 B

Variable types

Categorical2
Text2
Numeric6

Dataset

Description농산물유통정보(https://www.kamis.or.kr/)를 통해 제공되는 추석 성수기 주요 농축수산물의 가격동향 정보로 재료별 가격정보 제공
Author한국농수산식품유통공사
URLhttps://www.data.go.kr/data/15072479/fileData.do

Alerts

전통시장(1차조사) is highly overall correlated with 대형유통(1차조사) and 5 other fieldsHigh correlation
대형유통(1차조사) is highly overall correlated with 전통시장(1차조사) and 4 other fieldsHigh correlation
전통시장(2차조사) is highly overall correlated with 전통시장(1차조사) and 5 other fieldsHigh correlation
대형유통(2차조사) is highly overall correlated with 전통시장(1차조사) and 4 other fieldsHigh correlation
전통시장(3차조사) is highly overall correlated with 전통시장(1차조사) and 5 other fieldsHigh correlation
대형유통(3차조사) is highly overall correlated with 전통시장(1차조사) and 4 other fieldsHigh correlation
분류 is highly overall correlated with 구입단위High correlation
구입단위 is highly overall correlated with 전통시장(1차조사) and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 05:07:20.165411
Analysis finished2023-12-12 05:07:25.188003
Duration5.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
탕류
9 
기타
8 
과일류
5 
적류
4 
과자류
4 
Other values (3)
8 

Length

Max length3
Median length2
Mean length2.3947368
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송편
2nd row송편
3rd row적류
4th row적류
5th row적류

Common Values

ValueCountFrequency (%)
탕류 9
23.7%
기타 8
21.1%
과일류 5
13.2%
적류 4
10.5%
과자류 4
10.5%
나물류 3
 
7.9%
부재료 3
 
7.9%
송편 2
 
5.3%

Length

2023-12-12T14:07:25.298668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:07:25.460920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
탕류 9
23.7%
기타 8
21.1%
과일류 5
13.2%
적류 4
10.5%
과자류 4
10.5%
나물류 3
 
7.9%
부재료 3
 
7.9%
송편 2
 
5.3%

음식명
Text

Distinct25
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T14:07:25.679394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1842105
Min length1

Characters and Unicode

Total characters83
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)42.1%

Sample

1st row송편
2nd row송편
3rd row육적
4th row소적
5th row어적
ValueCountFrequency (%)
나물 3
 
7.9%
어탕 3
 
7.9%
육탕 3
 
7.9%
소탕 3
 
7.9%
송편 2
 
5.3%
나박김치 2
 
5.3%
녹두편 2
 
5.3%
식혜 2
 
5.3%
어적 2
 
5.3%
다식 1
 
2.6%
Other values (15) 15
39.5%
2023-12-12T14:07:26.009893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 9
 
10.8%
나 5
 
6.0%
어 5
 
6.0%
소 4
 
4.8%
육 4
 
4.8%
편 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
녹 2
 
2.4%
Other values (33) 40
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 9
 
10.8%
나 5
 
6.0%
어 5
 
6.0%
소 4
 
4.8%
육 4
 
4.8%
편 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
녹 2
 
2.4%
Other values (33) 40
48.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 9
 
10.8%
나 5
 
6.0%
어 5
 
6.0%
소 4
 
4.8%
육 4
 
4.8%
편 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
녹 2
 
2.4%
Other values (33) 40
48.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 9
 
10.8%
나 5
 
6.0%
어 5
 
6.0%
소 4
 
4.8%
육 4
 
4.8%
편 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
녹 2
 
2.4%
Other values (33) 40
48.2%

주재료
Text

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T14:07:26.262125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.5
Min length1

Characters and Unicode

Total characters95
Distinct characters59
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)73.7%

Sample

1st row쌀
2nd row참깨
3rd row소고기(우둔)
4th row두부(부침)
5th row동태포
ValueCountFrequency (%)
무 4
 
10.5%
쌀 3
 
7.9%
다시마 3
 
7.9%
북어 1
 
2.6%
배추 1
 
2.6%
게맛살 1
 
2.6%
밀가루 1
 
2.6%
산자 1
 
2.6%
약과 1
 
2.6%
강정 1
 
2.6%
Other values (21) 21
55.3%
2023-12-12T14:07:26.631391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
무 4
 
4.2%
기 4
 
4.2%
) 4
 
4.2%
다 4
 
4.2%
( 4
 
4.2%
시 4
 
4.2%
두 3
 
3.2%
고 3
 
3.2%
쌀 3
 
3.2%
마 3
 
3.2%
Other values (49) 59
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
91.6%
Close Punctuation 4
 
4.2%
Open Punctuation 4
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 4
 
4.6%
기 4
 
4.6%
다 4
 
4.6%
시 4
 
4.6%
두 3
 
3.4%
고 3
 
3.4%
쌀 3
 
3.4%
마 3
 
3.4%
부 3
 
3.4%
추 2
 
2.3%
Other values (47) 54
62.1%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
91.6%
Common 8
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 4
 
4.6%
기 4
 
4.6%
다 4
 
4.6%
시 4
 
4.6%
두 3
 
3.4%
고 3
 
3.4%
쌀 3
 
3.4%
마 3
 
3.4%
부 3
 
3.4%
추 2
 
2.3%
Other values (47) 54
62.1%
Common
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
91.6%
ASCII 8
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
무 4
 
4.6%
기 4
 
4.6%
다 4
 
4.6%
시 4
 
4.6%
두 3
 
3.4%
고 3
 
3.4%
쌀 3
 
3.4%
마 3
 
3.4%
부 3
 
3.4%
추 2
 
2.3%
Other values (47) 54
62.1%
ASCII
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

구입단위
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
150g내외
4 
400g
4 
1kg
4 
300g
3 
100g
3 
Other values (13)
20 

Length

Max length6
Median length5
Mean length3.6842105
Min length2

Unique

Unique9 ?
Unique (%)23.7%

Sample

1st row2kg
2nd row200g
3rd row1.8kg
4th row4모
5th row1kg

Common Values

ValueCountFrequency (%)
150g내외 4
10.5%
400g 4
10.5%
1kg 4
10.5%
300g 3
 
7.9%
100g 3
 
7.9%
10g 3
 
7.9%
200g 3
 
7.9%
1마리 3
 
7.9%
5개 2
 
5.3%
1.2kg 1
 
2.6%
Other values (8) 8
21.1%

Length

2023-12-12T14:07:26.802222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
150g내외 4
10.5%
1kg 4
10.5%
400g 4
10.5%
300g 3
 
7.9%
100g 3
 
7.9%
10g 3
 
7.9%
200g 3
 
7.9%
1마리 3
 
7.9%
5개 2
 
5.3%
2kg 1
 
2.6%
Other values (8) 8
21.1%

전통시장(1차조사)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6258.5789
Minimum157
Maximum75706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:07:26.948454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum157
5-th percentile157
Q11191
median3375
Q35884
95-th percentile14028.55
Maximum75706
Range75549
Interquartile range (IQR)4693

Descriptive statistics

Standard deviation12333.642
Coefficient of variation (CV)1.9706777
Kurtosis28.734717
Mean6258.5789
Median Absolute Deviation (MAD)2254.5
Skewness5.0807403
Sum237826
Variance1.5211873 × 108
MonotonicityNot monotonic
2023-12-12T14:07:27.134946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
195 3
 
7.9%
157 3
 
7.9%
5413 1
 
2.6%
3220 1
 
2.6%
1104 1
 
2.6%
6041 1
 
2.6%
8887 1
 
2.6%
7385 1
 
2.6%
13978 1
 
2.6%
14315 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
157 3
7.9%
195 3
7.9%
390 1
 
2.6%
994 1
 
2.6%
1104 1
 
2.6%
1137 1
 
2.6%
1353 1
 
2.6%
1354 1
 
2.6%
1515 1
 
2.6%
1587 1
 
2.6%
ValueCountFrequency (%)
75706 1
2.6%
14315 1
2.6%
13978 1
2.6%
13791 1
2.6%
13067 1
2.6%
10882 1
2.6%
10581 1
2.6%
8887 1
2.6%
7385 1
2.6%
6041 1
2.6%

대형유통(1차조사)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8864.3684
Minimum174
Maximum100407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:07:27.331025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum174
5-th percentile174
Q11388.75
median4392.5
Q311360.25
95-th percentile22817.55
Maximum100407
Range100233
Interquartile range (IQR)9971.5

Descriptive statistics

Standard deviation16490.178
Coefficient of variation (CV)1.8602767
Kurtosis26.999752
Mean8864.3684
Median Absolute Deviation (MAD)4003.5
Skewness4.8750858
Sum336846
Variance2.7192596 × 108
MonotonicityNot monotonic
2023-12-12T14:07:27.527351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
174 3
 
7.9%
356 3
 
7.9%
5169 1
 
2.6%
6796 1
 
2.6%
1965 1
 
2.6%
7420 1
 
2.6%
11817 1
 
2.6%
8363 1
 
2.6%
12873 1
 
2.6%
17243 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
174 3
7.9%
347 1
 
2.6%
356 3
7.9%
714 1
 
2.6%
1170 1
 
2.6%
1292 1
 
2.6%
1679 1
 
2.6%
1965 1
 
2.6%
2026 1
 
2.6%
2053 1
 
2.6%
ValueCountFrequency (%)
100407 1
2.6%
24090 1
2.6%
22593 1
2.6%
17243 1
2.6%
15680 1
2.6%
12913 1
2.6%
12873 1
2.6%
12149 1
2.6%
11981 1
2.6%
11817 1
2.6%

전통시장(2차조사)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6294.8684
Minimum159
Maximum75656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:07:27.702504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum159
5-th percentile159
Q11191.75
median3416
Q35781.5
95-th percentile14294.6
Maximum75656
Range75497
Interquartile range (IQR)4589.75

Descriptive statistics

Standard deviation12328.168
Coefficient of variation (CV)1.9584473
Kurtosis28.633962
Mean6294.8684
Median Absolute Deviation (MAD)2287
Skewness5.0687799
Sum239205
Variance1.5198372 × 108
MonotonicityNot monotonic
2023-12-12T14:07:27.882171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
196 3
 
7.9%
159 3
 
7.9%
5423 1
 
2.6%
3220 1
 
2.6%
1098 1
 
2.6%
5901 1
 
2.6%
8984 1
 
2.6%
7480 1
 
2.6%
13977 1
 
2.6%
14315 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
159 3
7.9%
196 3
7.9%
392 1
 
2.6%
1098 1
 
2.6%
1121 1
 
2.6%
1137 1
 
2.6%
1356 1
 
2.6%
1399 1
 
2.6%
1500 1
 
2.6%
1644 1
 
2.6%
ValueCountFrequency (%)
75656 1
2.6%
14315 1
2.6%
14291 1
2.6%
13977 1
2.6%
13067 1
2.6%
10771 1
2.6%
10497 1
2.6%
8984 1
2.6%
7480 1
2.6%
5901 1
2.6%

대형유통(2차조사)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8993.3421
Minimum187
Maximum100590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:07:28.083126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile187
Q11392
median4320
Q39791.75
95-th percentile23567.3
Maximum100590
Range100403
Interquartile range (IQR)8399.75

Descriptive statistics

Standard deviation16624.983
Coefficient of variation (CV)1.8485879
Kurtosis26.118409
Mean8993.3421
Median Absolute Deviation (MAD)3955.5
Skewness4.7774531
Sum341747
Variance2.7639007 × 108
MonotonicityNot monotonic
2023-12-12T14:07:28.256821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
187 3
 
7.9%
356 3
 
7.9%
5169 1
 
2.6%
6863 1
 
2.6%
1960 1
 
2.6%
7336 1
 
2.6%
7905 1
 
2.6%
8363 1
 
2.6%
15852 1
 
2.6%
19158 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
187 3
7.9%
356 3
7.9%
373 1
 
2.6%
743 1
 
2.6%
1170 1
 
2.6%
1292 1
 
2.6%
1692 1
 
2.6%
1960 1
 
2.6%
2007 1
 
2.6%
2074 1
 
2.6%
ValueCountFrequency (%)
100590 1
2.6%
26017 1
2.6%
23135 1
2.6%
19158 1
2.6%
15852 1
2.6%
15574 1
2.6%
12630 1
2.6%
12189 1
2.6%
11894 1
2.6%
9974 1
2.6%

전통시장(3차조사)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6429.8158
Minimum161
Maximum76041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:07:28.447359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161
5-th percentile161
Q11235.75
median3417.5
Q35454
95-th percentile15684.5
Maximum76041
Range75880
Interquartile range (IQR)4218.25

Descriptive statistics

Standard deviation12498.348
Coefficient of variation (CV)1.9438112
Kurtosis27.393048
Mean6429.8158
Median Absolute Deviation (MAD)2145.5
Skewness4.9336382
Sum244333
Variance1.562087 × 108
MonotonicityNot monotonic
2023-12-12T14:07:28.626477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
196 3
 
7.9%
161 3
 
7.9%
5482 1
 
2.6%
3220 1
 
2.6%
1093 1
 
2.6%
5370 1
 
2.6%
8708 1
 
2.6%
7565 1
 
2.6%
17767 1
 
2.6%
15317 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
161 3
7.9%
196 3
7.9%
391 1
 
2.6%
1093 1
 
2.6%
1137 1
 
2.6%
1191 1
 
2.6%
1370 1
 
2.6%
1399 1
 
2.6%
1500 1
 
2.6%
1678 1
 
2.6%
ValueCountFrequency (%)
76041 1
2.6%
17767 1
2.6%
15317 1
2.6%
15227 1
2.6%
13161 1
2.6%
10647 1
2.6%
10397 1
2.6%
8708 1
2.6%
7565 1
2.6%
5482 1
2.6%

대형유통(3차조사)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9010.4211
Minimum174
Maximum99293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:07:28.838088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum174
5-th percentile174
Q11399.25
median4261
Q39845.75
95-th percentile24091.55
Maximum99293
Range99119
Interquartile range (IQR)8446.5

Descriptive statistics

Standard deviation16532.827
Coefficient of variation (CV)1.8348562
Kurtosis25.127332
Mean9010.4211
Median Absolute Deviation (MAD)3905
Skewness4.6693805
Sum342396
Variance2.7333437 × 108
MonotonicityNot monotonic
2023-12-12T14:07:29.011280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
174 3
 
7.9%
356 3
 
7.9%
5169 1
 
2.6%
6893 1
 
2.6%
1836 1
 
2.6%
7000 1
 
2.6%
7699 1
 
2.6%
8363 1
 
2.6%
18591 1
 
2.6%
17796 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
174 3
7.9%
347 1
 
2.6%
356 3
7.9%
734 1
 
2.6%
1171 1
 
2.6%
1292 1
 
2.6%
1721 1
 
2.6%
1836 1
 
2.6%
1953 1
 
2.6%
2091 1
 
2.6%
ValueCountFrequency (%)
99293 1
2.6%
28764 1
2.6%
23267 1
2.6%
18591 1
2.6%
17796 1
2.6%
14868 1
2.6%
12100 1
2.6%
11989 1
2.6%
11832 1
2.6%
9974 1
2.6%

Interactions

2023-12-12T14:07:23.934917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:20.740151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.377467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.995434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.667997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.299495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:24.361124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:20.860904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.485440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.109013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.776407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.413944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:24.447599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:20.946818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.593281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.218378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.876076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.510629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:24.559464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.050116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.698651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.328391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.995229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.615699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:24.653200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.155871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.785532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.453356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.086058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.732538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:24.756420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.278802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:21.890847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:22.554587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.191380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:23.837418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:07:29.123043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류음식명주재료구입단위전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)
분류1.0001.0000.9530.9080.2980.5930.2980.5170.0000.517
음식명1.0001.0000.9690.8150.8990.6960.8990.6650.9460.665
주재료0.9530.9691.0000.9211.0001.0001.0001.0001.0001.000
구입단위0.9080.8150.9211.0000.9430.7880.9430.7830.8610.783
전통시장(1차조사)0.2980.8991.0000.9431.0000.7931.0000.7611.0000.761
대형유통(1차조사)0.5930.6961.0000.7880.7931.0000.7930.9990.9470.999
전통시장(2차조사)0.2980.8991.0000.9431.0000.7931.0000.7611.0000.761
대형유통(2차조사)0.5170.6651.0000.7830.7610.9990.7611.0000.9391.000
전통시장(3차조사)0.0000.9461.0000.8611.0000.9471.0000.9391.0000.939
대형유통(3차조사)0.5170.6651.0000.7830.7610.9990.7611.0000.9391.000
2023-12-12T14:07:29.263573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류구입단위
분류1.0000.555
구입단위0.5551.000
2023-12-12T14:07:29.388874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)분류구입단위
전통시장(1차조사)1.0000.9430.9990.9470.9980.9450.2200.663
대형유통(1차조사)0.9431.0000.9440.9940.9410.9940.2920.443
전통시장(2차조사)0.9990.9441.0000.9460.9990.9440.2200.663
대형유통(2차조사)0.9470.9940.9461.0000.9430.9980.2500.438
전통시장(3차조사)0.9980.9410.9990.9431.0000.9420.0000.523
대형유통(3차조사)0.9450.9940.9440.9980.9421.0000.2500.438
분류0.2200.2920.2200.2500.0000.2501.0000.555
구입단위0.6630.4430.6630.4380.5230.4380.5551.000

Missing values

2023-12-12T14:07:24.915877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:07:25.117850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

분류음식명주재료구입단위전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)
0송편송편쌀2kg541351695423516954825169
1송편송편참깨200g417589424208873342129461
2적류육적소고기(우둔)1.8kg75706100407756561005907604199293
3적류소적두부(부침)4모428212913450412630450411989
4적류어적동태포1kg108821568010771155741064714868
5적류어적계란10개158720531644207416782091
6탕류육탕소고기(양지)300g130672259313067231351316123267
7탕류육탕무100g195174196187196174
8탕류육탕다시마10g157356159356161356
9탕류소탕두부(찌개)1모113732121137315011372991
분류음식명주재료구입단위전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)
28과일류곶감곶감9개738583637480836375658363
29과일류배배5개139781287313977158521776718591
30과일류사과사과5개143151724314315191581531717796
31과자류다식다식150g내외322067963220686332206893
32과자류강정강정150g내외296356402963570630375595
33과자류약과약과150g내외151516791500169215001721
34과자류산자산자150g내외356842623578435935464375
35부재료밀가루밀가루1kg135411701399117013991171
36부재료게맛살게맛살300g188820261902200719021953
37부재료청주청주1.8L105819990104979974103979974